The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for generating a cognitive traits avatar that can be used to perform similarity matching between entities, e.g., persons, places, things, or the like.
With the proliferation of information available on computing networks, such as the Internet, users look to these computer networks as a primary source of information, a significant source of social interaction, and a means by which users are able to obtain products and services. Most people in today's society utilize such computer networks to perform various forms of commerce, on-line electronic commerce (e-commerce), using online commerce websites such as Amazon™, Ebay™, and the like, as well as to find and communicate with other persons in a business and social manner, e.g., social media and networking websites such as Facebook™, Instagram™, LinkedIn™, and the like.
A key type of information that users often look for when engaging in e-commerce or social media interactions is ratings of persons, products, or services. A known mechanism for rating persons, products, or services is to elicit user feedback regarding the persons, products, or services and then displaying a representation of the feedback score for the persons, products, or services, where the feedback score is an aggregate of the user feedback obtained from a plurality of users on a fixed scale. For example, often times this feedback score is represented graphically as a rating along a fixed scale of 1 to 5 stars with the stars being a representation of the aggregate user feedback score. Similar feedback mechanisms may present percentages of “thumbs up” or positive reviews and percentages of “thumbs down” or negative reviews. Such feedback scores may be provided in a number of different categories of information.
While such rating scales provide a good quick metric for evaluating a person, product, or service, there is very little in such a scoring mechanism that can be applied in a personalized way to a particular user. That is, the rating is generic in nature and does not necessarily identify the compatibility of person, product, or service with a particular person's own traits.